Nowcasting GDP using machine learning methods

被引：0

作者：

Kant, Dennis ^{[1
]}

Pick, Andreas ^{[1
]}

De Winter, Jasper ^{[2
]}

机构：

[1] Erasmus Univ, Rotterdam, Netherlands

[2] De Nederlandsche Bank, Amsterdam, Netherlands

来源：

ASTA-ADVANCES IN STATISTICAL ANALYSIS | 2025年 / 109卷 / 01期

关键词：

Factor models; Forecasting competition; Machine learning methods; Nowcasting; C32; C53; E37; FACTOR MODEL; MIDAS; SELECTION;

D O I：

10.1007/s10182-024-00515-0

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This paper compares the ability of several econometric and machine learning methods to nowcast GDP in (pseudo) real-time. The analysis takes the example of Dutch GDP over the period 1992Q1-2018Q4 using a broad data set of monthly indicators. It discusses the forecast accuracy but also analyzes the use of information from the large data set of macroeconomic and financial predictors. We find that, on average, the random forest provides the most accurate forecast and nowcasts, whilst the dynamic factor model provides the most accurate backcasts.

引用

页码：1 / 24

页数：24

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